一级黄色片免费播放|中国黄色视频播放片|日本三级a|可以直接考播黄片影视免费一级毛片

高級(jí)搜索

留言板

尊敬的讀者、作者、審稿人, 關(guān)于本刊的投稿、審稿、編輯和出版的任何問題, 您可以本頁添加留言。我們將盡快給您答復(fù)。謝謝您的支持!

姓名
郵箱
手機(jī)號(hào)碼
標(biāo)題
留言內(nèi)容
驗(yàn)證碼

基于模糊子空間聚類的〇階L2型TSK模糊系統(tǒng)

鄧趙紅 張江濱 蔣亦樟 史熒中 王士同

鄧趙紅, 張江濱, 蔣亦樟, 史熒中, 王士同. 基于模糊子空間聚類的〇階L2型TSK模糊系統(tǒng)[J]. 電子與信息學(xué)報(bào), 2015, 37(9): 2082-2088. doi: 10.11999/JEIT150074
引用本文: 鄧趙紅, 張江濱, 蔣亦樟, 史熒中, 王士同. 基于模糊子空間聚類的〇階L2型TSK模糊系統(tǒng)[J]. 電子與信息學(xué)報(bào), 2015, 37(9): 2082-2088. doi: 10.11999/JEIT150074
Deng Zhao-hong, Zhang Jiang-bin, Jiang Yi-zhang, Shi Ying-zhong, Wang Shi-tong. Fuzzy Subspace Clustering Based Zero-order L2-norm TSK Fuzzy System[J]. Journal of Electronics & Information Technology, 2015, 37(9): 2082-2088. doi: 10.11999/JEIT150074
Citation: Deng Zhao-hong, Zhang Jiang-bin, Jiang Yi-zhang, Shi Ying-zhong, Wang Shi-tong. Fuzzy Subspace Clustering Based Zero-order L2-norm TSK Fuzzy System[J]. Journal of Electronics & Information Technology, 2015, 37(9): 2082-2088. doi: 10.11999/JEIT150074

基于模糊子空間聚類的〇階L2型TSK模糊系統(tǒng)

doi: 10.11999/JEIT150074
基金項(xiàng)目: 

國家自然科學(xué)基金(61170122),江蘇省杰出青年基金(BK20140001)和新世紀(jì)優(yōu)秀人才支持計(jì)劃(NCET120882)

Fuzzy Subspace Clustering Based Zero-order L2-norm TSK Fuzzy System

  • 摘要: 經(jīng)典數(shù)據(jù)驅(qū)動(dòng)型TSK(Takagi-Sugeno-Kang)模糊系統(tǒng)在獲取模糊規(guī)則時(shí),會(huì)考慮數(shù)據(jù)的所有特征空間,其帶來一個(gè)重要缺陷:如果數(shù)據(jù)的特征空間維數(shù)過高,則系統(tǒng)獲取的模糊規(guī)則繁雜,使系統(tǒng)復(fù)雜度增加而導(dǎo)致解釋性下降。該文針對(duì)此缺陷,探討了一種基于模糊子空間聚類的〇階L2型TSK模糊系統(tǒng)(Fuzzy Subspace Clustering based zero-order L2- norm TSK Fuzzy System, FSC-0-L2-TSK-FS)構(gòu)建新方法。新方法構(gòu)建的模糊系統(tǒng)不僅能縮減模糊規(guī)則前件的特征空間,而且獲取的模糊規(guī)則可對(duì)應(yīng)于不同的特征子空間,從而具有更接近人類思維的推理機(jī)制。模擬和真實(shí)數(shù)據(jù)集上的建模結(jié)果表明,新方法增強(qiáng)了面對(duì)高維數(shù)據(jù)所建模型的解釋性,同時(shí)所建模型得到了較之于一些經(jīng)典方法更好或可比較的泛化性能。
  • Zadeh L A. Fuzzy sets[J]. Information and Control, 1965, 8(3): 338-353.
    李奕, 吳小俊. 基于監(jiān)督學(xué)習(xí)的Takagi Sugeno Kang模糊系統(tǒng)圖像融合方法研究[J]. 電子與信息學(xué)報(bào), 2014, 36(5): 1126-1132.
    Li Yi and Wu Xiao-jun. A novel image fusion method using the Takagi Sugeno Kang fuzzy system based on supervised learning[J]. Journal of Electronics Information Technology, 2014, 36(5): 1126-1132.
    宋恒, 王晨, 馬時(shí)平, 等. 基于非單點(diǎn)模糊支持向量機(jī)的判決反饋均衡器[J]. 電子與信息學(xué)報(bào), 2008, 30(1): 117-120.
    Song Heng, Wang Chen, Ma Shi-ping, et al.. A decision feedback equalizer based on non-singleton fuzzy support vector machine[J]. Journal of Electronics Information Technology, 2008, 30(1): 117-120.
    Lughofer E. On-line assurance of interpretability criteria in evolving fuzzy systemsachievements, new concepts and open issues[J]. Information Sciences, 2013(251): 22-46.
    Riid A and Rstern E. Adaptability, interpretability and rule weights in fuzzy rule-based systems[J]. Information Sciences, 2014(257): 301-312.
    Thong N T and Son L H. HIFCF: an effective hybrid model between picture fuzzy clustering and intuitionistic fuzzy recommender systems for medical diagnosis[J]. Expert System with Application, 2015, 42(7): 3682-3701.
    Sanz J A, Galar M, Jurio A, et al.. Medical diagnosis of cardiovascular diseases using an interval-valued fuzzy rule-based classification system[J]. Applied Soft Computing, 2014(20): 103-111.
    Takagi T and Sugeno M. Fuzzy identification of systems and its applications to modeling and control[J]. IEEE Transactions on Systems, Man and Cybernetics, 1985(1): 116-132.
    Sugeno M and Kang G T. Structure identification of fuzzy model[J]. Fuzzy Sets and Systems, 1988, 28(1): 15-33.
    Jiang Yi-zhang, Chung Fu-lai, Ishibuchi H, et al.. Multitask TSK fuzzy system modeling by mining intertask common hidden structure[J]. IEEE Transactions on Cybernetics, 2015, 45(3): 548-561.
    Fadali S and Jafarzadeh S. TSK observers for discrete type-1 and type-2 fuzzy systems[J]. IEEE Transactions on Fuzzy Systems, 2014, 22(2): 451-458.
    Chung Fu-lai, Deng Zhao-hong, and Wang Shi-tong. From minimum enclosing ball to fast fuzzy inference system training on large datasets[J]. IEEE Transactions on Fuzzy Systems, 2009, 17(1): 173-184.
    Mamdani E H. Application of fuzzy logic to approximate reasoning using linguistic synthesis[J]. IEEE Transactions on Computers, 1977, 100(12): 1182-1191.
    Azeem M F, Hanmandlu M, and Ahmad N. Generalization of adaptive neuro-fuzzy inference systems[J]. IEEE Transactions on Neural Networks, 2000, 11(6): 1332-1346.
    Gan Guo-jun and Wu Jian-hong. A convergence theorem for the fuzzy subspace clustering (FSC) algorithm[J]. Pattern Recognition, 2008, 41(6): 1939-1947.
    Deng Zhao-hong, Choi Kup-sze, Chung Fu-lai, et al.. Enhanced soft subspace clustering integrating within-cluster and between-cluster information[J]. Pattern Recognition, 2010, 43(3): 767-781.
    Leski J M. TSK-fuzzy modeling based on -insensitive learning[J]. IEEE Transactions on Fuzzy Systems, 2005, 13(2): 181-193.
    Deng Zhao-hong, Choi Kup-sze, Chung Fu-lai, et al.. Scalable TSK fuzzy modeling for very large datasets using minimal- enclosing-ball approximation[J]. IEEE Transactions on Fuzzy Systems, 2011, 19(2): 210-226.
    Juang Chia-feng and Chiang Loa. Zero-order TSK-type fuzzy system learning using a two-phase swarm intelligence algorithm[J]. Fuzzy Sets and Systems, 2008, 159(21): 2910-2926.
    Tsang I W, Kwok J T Y, and Zurada J M. Generalized core vector machines[J]. IEEE Transactions on Neural Networks, 2006, 17(5): 1126-1140.
  • 加載中
計(jì)量
  • 文章訪問數(shù):  1394
  • HTML全文瀏覽量:  149
  • PDF下載量:  476
  • 被引次數(shù): 0
出版歷程
  • 收稿日期:  2015-01-13
  • 修回日期:  2015-05-11
  • 刊出日期:  2015-09-19

目錄

    /

    返回文章
    返回